OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis

TL;DR

OpenAPMax is a novel open-set recognition model for Alzheimer's diagnosis that leverages abnormal pattern analysis and extreme value theory to improve real-world clinical classification accuracy.

Contribution

The paper introduces OpenAPMax, a new open-set recognition approach tailored for Alzheimer's diagnosis, addressing the challenge of distinguishing degenerative stages with similar symptoms.

Findings

Achieved state-of-the-art results in open-set recognition for AD.

Effectively models abnormal patterns relative to known categories.

Improves early diagnosis accuracy in clinical settings.

Abstract

Alzheimer's disease (AD) cannot be reversed, but early diagnosis will significantly benefit patients' medical treatment and care. In recent works, AD diagnosis has the primary assumption that all categories are known a prior -- a closed-set classification problem, which contrasts with the open-set recognition problem. This assumption hinders the application of the model in natural clinical settings. Although many open-set recognition technologies have been proposed in other fields, they are challenging to use for AD diagnosis directly since 1) AD is a degenerative disease of the nervous system with similar symptoms at each stage, and it is difficult to distinguish from its pre-state, and 2) diversified strategies for AD diagnosis are challenging to model uniformly. In this work, inspired by the concerns of clinicians during diagnosis, we propose an open-set recognition model, OpenAPMax, based on the anomaly pattern to address AD diagnosis in real-world settings. OpenAPMax first obtains the abnormal pattern of each patient relative to each known category through statistics or a literature search, clusters the patients' abnormal pattern, and finally, uses extreme value theory (EVT) to model the distance between each patient's abnormal pattern and the center of their category and modify the classification probability. We evaluate the performance of the proposed method with recent open-set recognition, where we obtain state-of-the-art results.